{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "\n",
    "# 定义公共编码器，与之前示例相同\n",
    "class CommonEncoder(nn.Module):\n",
    "    def __init__(self, input_dim, output_dim):\n",
    "        super(CommonEncoder, self).__init__()\n",
    "        self.fc = nn.Linear(input_dim, output_dim)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        return self.fc(x)\n",
    "\n",
    "# 假设的模型结构，包含公共编码器和一些其他部分（这里简化为一个线性层作为示例）\n",
    "class MultiModalSentimentModel(nn.Module):\n",
    "    def __init__(self, text_dim, audio_dim, video_dim, common_dim, output_dim):\n",
    "        super(MultiModalSentimentModel, self).__init__()\n",
    "        self.common_encoder = CommonEncoder(max(text_dim, audio_dim, video_dim), common_dim)\n",
    "        # 这里简化处理，只使用公共表示作为输入，实际中可能会更复杂\n",
    "        self.sentiment_classifier = nn.Linear(common_dim, output_dim)\n",
    "    \n",
    "    def forward(self, text_features, audio_features, video_features):\n",
    "        # 公共编码器处理所有模态的特征\n",
    "        common_text_rep = self.common_encoder(text_features)\n",
    "        common_audio_rep = self.common_encoder(audio_features)\n",
    "        common_video_rep = self.common_encoder(video_features)\n",
    "    \n",
    "        # 这里简化处理，只使用文本模态的公共表示作为示例\n",
    "        # 实际中可能需要融合所有模态的表示\n",
    "        combined_rep = common_text_rep\n",
    "    \n",
    "        # 情感分类器进行分类\n",
    "        sentiment_prediction = self.sentiment_classifier(combined_rep)\n",
    "        return sentiment_prediction\n",
    "\n",
    "# 初始化模型、损失函数和优化器\n",
    "text_dim = 768\n",
    "audio_dim = 16\n",
    "video_dim = 32\n",
    "common_dim = 128\n",
    "output_dim = 1  # 假设是回归任务，输出情感强度\n",
    "\n",
    "model = MultiModalSentimentModel(text_dim, audio_dim, video_dim, common_dim, output_dim)\n",
    "criterion = nn.MSELoss()  # 假设是回归任务，使用均方误差损失\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
    "\n",
    "# 模拟数据和标签\n",
    "batch_size = 32\n",
    "text_features = torch.randn(batch_size, text_dim)\n",
    "audio_features = torch.randn(batch_size, audio_dim)\n",
    "video_features = torch.randn(batch_size, video_dim)\n",
    "labels = torch.randn(batch_size, output_dim)  # 模拟的情感标签\n",
    "\n",
    "# 训练循环（简化版）\n",
    "num_epochs = 10\n",
    "for epoch in range(num_epochs):\n",
    "    # 前向传播\n",
    "    outputs = model(text_features, audio_features, video_features)\n",
    "    loss = criterion(outputs, labels)\n",
    "  \n",
    "    # 反向传播和优化\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "  \n",
    "    # 打印统计信息（可选）\n",
    "    if (epoch+1) % 2 == 0:\n",
    "        print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item()}')\n",
    "\n",
    "# 训练完成后，公共编码器的参数已经被更新了\n",
    "print(\"Common encoder parameters have been updated during training.\")\n",
    "print(model.common_encoder.fc.weight)  # 查看公共编码器全连接层的权重"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
